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To solve the problem of the redundant number of training samples in a correlation filter-based tracking algorithm, the training samples were implicitly extended by circular shifts of the given target patches, and all the extended samples were used as negative samples for the fast online learning of the filter. Since all these shifted patches were not true negative samples of the target, the tracking process suffered from boundary effects, especially in challenging situations such as occlusion and background clutter, which can significantly impair the tracking performance of the tracker. Spatial regularization in the SRDCF tracking algorithm is an effective way to mitigate boundary effects, but it comes at the cost of highly increased time complexity, resulting in a very slow tracking speed of the SRDCF algorithm that cannot achieve a real-time tracking effect. To address this issue, we proposed a fast-tracking algorithm based on spatially regularized correlation filters that efficiently optimized the solved filters by replacing the Gauss–Seidel method in the SRDCF algorithm with the alternating direction multiplier method. The problem of slow speed in the SRDCF tracking algorithm improved, and the improved FSRCF algorithm achieved real-time tracking. An adaptive update mechanism was proposed by using the feedback from the high confidence tracking results to avoid model corruption. That is, a robust confidence evaluation criterion was introduced in the model update phase, which combined the maximum response criterion and the average peak correlation energy APCE criterion to determine whether to update the filter, thereby avoiding filter model drift and improving the target tracking accuracy and speed. We conducted extensive experiments on datasets OTB-2015, OTB-2013, UAV123, and TC128, and the experimental results show that the proposed algorithm exhibits a more stable and accurate tracking performance in the presence of occlusion and background clutter during tracking. [ABSTRACT FROM AUTHOR]

Aiming at the problems of target model drift or loss of target tracking caused by serious deformation, occlusion, fast motion, and out of view of the target in long-term moving target tracking in complex scenes, this paper presents a robust multi-feature single-target tracking algorithm based on a particle filter. The algorithm is based on the correlation filtering framework. First, to extract more accurate target appearance features, in addition to the manual features histogram of oriented gradient features and color histogram features, the depth features from the conv3–4, conv4–4 and conv5–4 convolutional layer outputs in VGGNet-19 are also fused. Secondly, this paper designs a re-detection module of a fusion particle filter for the problem of how to return to accurate tracking after the target tracking fails, so that the algorithm in this paper can maintain high robustness during long-term tracking. Finally, in the adaptive model update stage, the adaptive learning rate update and adaptive filter update are performed to improve the accuracy of target tracking. Extensive experiments are conducted on dataset OTB-2015, dataset OTB-2013, and dataset UAV123. The experimental results show that the proposed multi-feature single-target robust tracking algorithm with fused particle filtering can effectively solve the long-time target tracking problem in complex scenes, while showing more stable and accurate tracking performance. [ABSTRACT FROM AUTHOR]

• Proposing a framework for semantic trajectory clustering based on community detection. • Introducing the ontology theory to semantic trajectory similarity computation. • Constructing a trajectory network according to the semantic trajectory similarity. • Conducting extensive studies and analyses on three real world trajectory datasets. • Comparing the proposed method with some traditional and recently proposed methods. Most of traditional trajectory clustering algorithms often cluster similar trajectories from a temporal or spatial perspective. One weak point is that the semantic relationship between the trajectories is ignored. In some cases, trajectories with spatio-temporal similarities may be semantically related, and the negligence of semantic information may result in unreasonable trajectory clustering results. In addition, the existing semantic trajectory clustering algorithms only consider the local semantic relationship between adjacent spatio-temporal trajectories, and the overall global semantic relationship between trajectories is still unknown. Considering the disadvantages of the current trajectory clustering methods, we proposed a novel algorithm for semantic trajectory clustering based on community detection (STCCD) in networks, which can better measure the semantic similarity of trajectories and capture global relationship among trajectories from the perspective of the network, and can get better trajectory clustering results compared to some traditional and recently proposed methods. Experimental results demonstrate that the proposed method can effectively mine the trajectory clustering information and related knowledge from the semantic trajectory data. [ABSTRACT FROM AUTHOR]